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Sean Murdock,
Matt Swaffer,
Ben Goldberg,
Amanda Moran,
and
Valerie Scarlata
Learn Spark & Data Lakes with Udacity's online course. Learn about the big data ecosystem and the power of Apache Spark for data wrangling and transformation.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Amazon web services basics
- Database fundamentals
- Intermediate Python
- Intermediate SQL
- Data modeling basics
Read more
Learn Spark & Data Lakes with Udacity's online course. Learn about the big data ecosystem and the power of Apache Spark for data wrangling and transformation.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Amazon web services basics
- Database fundamentals
- Intermediate Python
- Intermediate SQL
- Data modeling basics
Read more
Learn Spark & Data Lakes with Udacity's online course. Learn about the big data ecosystem and the power of Apache Spark for data wrangling and transformation.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Amazon web services basics
- Database fundamentals
- Intermediate Python
- Intermediate SQL
- Data modeling basics
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/xribga/spark
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What's inside
Syllabus
In this course you'll learn how Spark evaluates code and uses distributed computing to process and transform data. You'll work in the big data ecosystem to build data lakes and data lake houses.
Read more
Syllabus
In this course you'll learn how Spark evaluates code and uses distributed computing to process and transform data. You'll work in the big data ecosystem to build data lakes and data lake houses.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Builds essential data science skills in Apache Spark
Focuses on practical applications of Apache Spark for big data wrangling and transformation
Provides hands-on experience through projects and labs
Taught by experienced instructors in the data science field
Course highlights the power of Spark for processing and transforming data in a distributed computing environment
Insufficient information available on potential prerequisites or recommendations for the program
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Reviews summary
Hands-on spark and aws data lakes
According to learners, this course offers a highly practical and foundational understanding of Spark and modern data lake architectures, particularly emphasizing its integration with AWS services like S3, Glue, and Athena. Students frequently commend the hands-on projects, especially the final one, for effectively cementing concepts in PySpark and Spark SQL. While the course is broadly seen as a strong stepping stone for data engineering roles, a few reviewers note that the pacing can be uneven and some topics could benefit from deeper dives into advanced optimization. A consistent theme is the critical importance of meeting the stated prerequisites, as learners without solid intermediate Python, SQL, and AWS basics may find the course challenging due to its advanced nature.
Offers a solid foundation in Spark, PySpark, Spark SQL, and AWS integration.
"This course was absolutely fantastic for getting a solid grip on Spark and understanding the modern data lake architecture."
"The coverage of PySpark and Spark SQL was particularly useful. For a foundational course, it delivers well on its promises."
"The explanations of distributed computing and the big data ecosystem were clear. Working with AWS Glue and S3 for building data lake zones was super relevant to real-world scenarios."
The course excels with practical, real-world projects.
"The hands-on projects, especially the final one involving sensor data and AWS Glue, were incredibly practical and cemented my understanding."
"Working with AWS Glue and S3 for building data lake zones was super relevant to real-world scenarios. The explanations of distributed computing were clear."
"Highly recommend this course for anyone looking to understand Spark and data lakes from a practical perspective. The explanations were clear, and the hands-on labs were invaluable."
Some learners encountered issues with lab setup and stability.
"The labs mostly worked, though a few had minor setup issues that required some troubleshooting."
"Sometimes the lab environment was a bit flaky, but generally manageable."
"The labs were often frustrating to set up, and I spent more time debugging environments than learning Spark. Perhaps it's better for those with more professional experience in cloud environments."
Some topics felt rushed, lacking deeper dives for advanced learners.
"My only minor gripe is that some topics felt a little rushed, and I would have liked deeper dives into optimization strategies for larger datasets."
"The course has good content but the pacing felt a bit off. Some sections were too slow for me while others sped through complex ideas."
"I think the material could benefit from being updated more frequently, as Spark and AWS evolve quickly. Also, the support forums could be more active."
Prior knowledge in Python, SQL, and AWS is essential.
"It assumes you know the prerequisites like Python and SQL, but if you do, it's a smooth ride."
"It's definitely not for true beginners; you really need solid Python, SQL, and AWS knowledge coming in."
"I struggled with this course. While the topics are important, I felt the prerequisites were understated. Despite having 'intermediate' Python, I found some of the Spark concepts quite advanced without more foundational Big Data knowledge."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Spark and Data Lakes with these
activities:
Review SQL syntax
Show steps
Review SQL syntax before starting the course to refresh your memory and ensure you have a strong foundation.
Browse courses on
SQL
Show steps
-
Read through SQL syntax documentation
Follow Python for Data Analysis tutorial
Show steps
Work through a Python for Data Analysis tutorial to enhance your Python skills and prepare for data wrangling and transformation tasks.
Browse courses on
Python
Show steps
-
Find a reputable Python for Data Analysis tutorial
-
Follow the tutorial step-by-step
-
Practice the exercises and examples provided
Solve Spark SQL practice problems
Show steps
Engage in Spark SQL practice problems to solidify your understanding of data wrangling and transformation techniques.
Browse courses on
Spark SQL
Show steps
-
Find a collection of Spark SQL practice problems
-
Attempt to solve the problems independently
-
Review solutions and identify areas for improvement
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Attend a Spark workshop
Show steps
Attend a Spark workshop to expand your knowledge and gain practical experience with Spark in a structured environment.
Browse courses on
Spark
Show steps
-
Find a Spark workshop that aligns with your interests
-
Register and attend the workshop
-
Actively participate in discussions and exercises
Practice Data Manipulation in PySpark
Show steps
Provides practical experience in working with PySpark and solidifies understanding of data manipulation techniques.
Browse courses on
Pyspark
Show steps
-
Create a PySpark session and load sample data.
-
Use PySpark's DataFrame API to explore and manipulate data.
-
Perform data filtering, aggregation, and transformations.
Follow Spark Performance Optimization Tutorials
Show steps
Provides insights and techniques to improve Spark application performance and efficiency.
Show steps
-
Explore Spark performance tuning best practices.
-
Identify and address common performance bottlenecks.
-
Learn techniques for optimizing memory usage and data locality.
Attend a Spark Workshop on Data Engineering
Show steps
Provides a facilitated learning environment where participants can engage with experts and apply their knowledge in real-world scenarios.
Browse courses on
Data Engineering
Show steps
-
Register for the workshop and prepare for hands-on activities.
-
Engage with industry professionals and learn about best practices.
-
Work on practical exercises and receive feedback from experts.
Mentor a junior data engineer
Show steps
Share your knowledge by mentoring a junior data engineer, providing guidance and support that will enhance their understanding of the course material.
Browse courses on
Mentoring
Show steps
-
Identify a junior data engineer who would benefit from your guidance
-
Establish regular communication and support sessions
-
Provide feedback and encouragement on their progress
Build a data pipeline with Python
Show steps
Create a data pipeline with Python to gain practical experience in data engineering and reinforce concepts learned in the course.
Browse courses on
Data Pipelines
Show steps
-
Define the data pipeline requirements
-
Design the pipeline architecture
-
Implement the pipeline using Python
-
Test and validate the pipeline
Build a Data Pipeline with AWS Glue
Show steps
Provides hands-on experience in designing and implementing a data pipeline using AWS Glue, solidifying understanding of data processing techniques and AWS ecosystem.
Browse courses on
AWS Glue
Show steps
-
Create AWS Glue crawlers and ETL jobs.
-
Configure data sources, transformations, and outputs.
-
Monitor and troubleshoot the data pipeline.
Participate in a data science hackathon
Show steps
Join a data science hackathon to apply your skills, collaborate with others, and expand your knowledge in a practical setting.
Browse courses on
Data Science
Show steps
-
Find a relevant data science hackathon
-
Form a team or work independently
-
Develop a solution to the hackathon challenge
Review SQL syntax
Show steps
Review SQL syntax before starting the course to refresh your memory and ensure you have a strong foundation.
Browse courses on
SQL
Show steps
Show steps
Show steps
- Read through SQL syntax documentation
Follow Python for Data Analysis tutorial
Show steps
Work through a Python for Data Analysis tutorial to enhance your Python skills and prepare for data wrangling and transformation tasks.
Browse courses on
Python
Show steps
Show steps
Show steps
- Find a reputable Python for Data Analysis tutorial
- Follow the tutorial step-by-step
- Practice the exercises and examples provided
Solve Spark SQL practice problems
Show steps
Engage in Spark SQL practice problems to solidify your understanding of data wrangling and transformation techniques.
Browse courses on
Spark SQL
Show steps
Show steps
Show steps
- Find a collection of Spark SQL practice problems
- Attempt to solve the problems independently
- Review solutions and identify areas for improvement
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Attend a Spark workshop
Show steps
Attend a Spark workshop to expand your knowledge and gain practical experience with Spark in a structured environment.
Browse courses on
Spark
Show steps
Show steps
Show steps
- Find a Spark workshop that aligns with your interests
- Register and attend the workshop
- Actively participate in discussions and exercises
Practice Data Manipulation in PySpark
Show steps
Provides practical experience in working with PySpark and solidifies understanding of data manipulation techniques.
Browse courses on
Pyspark
Show steps
Show steps
Show steps
- Create a PySpark session and load sample data.
- Use PySpark's DataFrame API to explore and manipulate data.
- Perform data filtering, aggregation, and transformations.
Follow Spark Performance Optimization Tutorials
Show steps
Provides insights and techniques to improve Spark application performance and efficiency.
Show steps
Show steps
Show steps
- Explore Spark performance tuning best practices.
- Identify and address common performance bottlenecks.
- Learn techniques for optimizing memory usage and data locality.
Attend a Spark Workshop on Data Engineering
Show steps
Provides a facilitated learning environment where participants can engage with experts and apply their knowledge in real-world scenarios.
Browse courses on
Data Engineering
Show steps
Show steps
Show steps
- Register for the workshop and prepare for hands-on activities.
- Engage with industry professionals and learn about best practices.
- Work on practical exercises and receive feedback from experts.
Mentor a junior data engineer
Show steps
Share your knowledge by mentoring a junior data engineer, providing guidance and support that will enhance their understanding of the course material.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify a junior data engineer who would benefit from your guidance
- Establish regular communication and support sessions
- Provide feedback and encouragement on their progress
Build a data pipeline with Python
Show steps
Create a data pipeline with Python to gain practical experience in data engineering and reinforce concepts learned in the course.
Browse courses on
Data Pipelines
Show steps
Show steps
Show steps
- Define the data pipeline requirements
- Design the pipeline architecture
- Implement the pipeline using Python
- Test and validate the pipeline
Build a Data Pipeline with AWS Glue
Show steps
Provides hands-on experience in designing and implementing a data pipeline using AWS Glue, solidifying understanding of data processing techniques and AWS ecosystem.
Browse courses on
AWS Glue
Show steps
Show steps
Show steps
- Create AWS Glue crawlers and ETL jobs.
- Configure data sources, transformations, and outputs.
- Monitor and troubleshoot the data pipeline.
Participate in a data science hackathon
Show steps
Join a data science hackathon to apply your skills, collaborate with others, and expand your knowledge in a practical setting.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a relevant data science hackathon
- Form a team or work independently
- Develop a solution to the hackathon challenge
Career center
Learners who complete Spark and Data Lakes will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Apache Spark is among the tools used by Data Scientists to build, train, and evaluate machine learning models. This course can help someone aiming for this career by providing a solid foundation in the core concepts of Apache Spark and how to use it to tackle a wide range of Big Data challenges.
Data Engineer
Data Engineer
Structured Query Language (SQL) is widely used in Data Engineering, and this course can help one aiming to become a Data Engineer by providing a solid foundation in Apache Spark, including various fundamental Spark SQL concepts.
Software Engineer
Software Engineer
Apache Spark is used in various software applications, especially those involving data at scale, making it a valuable skill for Software Engineers. This course, which teaches key concepts of Apache Spark and how to use it in distributed computing environments, can help someone targeting a career in Software Engineering.
Systems Engineer
Systems Engineer
Systems Engineers are involved in designing, deploying, and maintaining data systems, so a solid understanding of Apache Spark, which is widely used for large-scale data processing, can be an advantage. This course can provide a Systems Engineer with an introduction to this technology, helping them to be competitive in the job market.
Cloud Architect
Cloud Architect
Apache Spark is deployed on cloud platforms including AWS, and it is part of the skill set for Cloud Architects. This course can help someone aiming to become a Cloud Architect by offering an overview of Apache Spark and how to use it to build data lakes and data lake houses on AWS.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use Spark's machine learning library to prepare data for modeling, train models, and evaluate their performance. This course provides foundational knowledge of Apache Spark and how to use it for these tasks, which can be beneficial for someone pursuing a career in Machine Learning Engineering.
Business Analyst
Business Analyst
Business Analysts leverage Apache Spark to analyze large datasets and derive insights to support decision-making. This course can help one seeking a career as a Business Analyst by introducing them to the fundamentals of Apache Spark and how to use it for data wrangling and transformation.
Data Analyst
Data Analyst
Data Analysts use Apache Spark to explore, analyze, and interpret large datasets. This course can provide someone pursuing a career as a Data Analyst with a solid foundation in Apache Spark and how to use it for data analysis tasks.
Software Developer
Software Developer
Software Developers use Apache Spark to build data-intensive applications. This course can benefit someone aiming to become a Software Developer by providing them with an introduction to Apache Spark and its applications in software development.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts leverage Apache Spark to analyze large financial datasets and build models for risk assessment, trading strategies, and portfolio optimization. This course can provide someone pursuing a career as a Quantitative Analyst with an introduction to the fundamentals of Apache Spark and how to use it for these tasks.
Data Architect
Data Architect
Data Architects design and manage data systems, and a solid understanding of Apache Spark is beneficial in this role. This course can provide someone aiming to become a Data Architect with an introduction to Apache Spark and how to use it for large-scale data processing.
Database Administrator
Database Administrator
Database Administrators (DBAs) are responsible for managing and maintaining databases, which may include Apache Spark. This course can provide someone pursuing a career as a DBA with a basic understanding of Apache Spark.
Data Warehouse Engineer
Data Warehouse Engineer
Data Warehouse Engineers design, build, and maintain data warehouses, which often involve Apache Spark. This course can provide someone aspiring to become a Data Warehouse Engineer with an introduction to Apache Spark and its applications in data warehousing.
Infrastructure Engineer
Infrastructure Engineer
Infrastructure Engineers provide support for data systems, which may include Apache Spark. This course can benefit someone aiming to become an Infrastructure Engineer by giving them an understanding of Apache Spark and how to use it in distributed computing environments.
DevOps Engineer
DevOps Engineer
DevOps Engineers collaborate in the development and operation of software systems, and Apache Spark is often used in these systems. This course can provide someone pursuing a career as a DevOps Engineer with an introduction to Apache Spark and how to use it in software development and operations.
For more career information including salaries, visit:
OpenCourser.com/course/xribga/spark
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Spark and Data Lakes.
Save
Provides a comprehensive introduction to Apache Spark, covering its core concepts, features, and applications. It is written by some of the original creators of Spark, ensuring its accuracy and relevance to the course.
Is the definitive guide to Apache Spark, written by its original creators. It provides a comprehensive overview of Spark, its architecture, and its applications. It is an excellent resource for both beginners and experienced Spark users.
A must-have reference for Python usage in data analysis, covering essential libraries such as NumPy, Pandas, and Matplotlib.
Focuses on advanced analytics with Spark, covering topics such as machine learning, graph processing, and data exploration. It provides practical examples and exercises, extending the course's coverage of Spark's capabilities.
A comprehensive guide to data lake implementation and management, providing best practices and industry insights.
Provides a comprehensive overview of the Hadoop ecosystem, including HDFS, MapReduce, and YARN. While not directly focused on Spark, it offers a valuable foundation for understanding the context in which Spark operates.
Save
Ideal as a supplement, this book covers practical applications of Spark in various domains, such as real-time stream processing.
Covers AWS services used in conjunction with Apache Spark, providing a practical reference for AWS integration.
A beginner-friendly introduction to data lakes, covering their benefits, challenges, and best practices.
While not specific to Spark or data lakes, this book provides valuable insights into the business applications of data analysis and modeling.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/xribga/spark
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Effort
4 weeks
Level
Intermediate
Via
Udacity
Instructors
Sean Murdock
Matt Swaffer
Ben Goldberg
Amanda Moran
Valerie Scarlata
Language
English
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